





"""simple docstring"""





import enum
import shutil
import sys


SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__   =  shutil.get_terminal_size()

SCREAMING_SNAKE_CASE__   =  {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}

class 						lowercase   (   enum.Enum   ):
      _SCREAMING_SNAKE_CASE							      = 0
      _SCREAMING_SNAKE_CASE							      = 1







def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		Union[str, Any]=""     ):







      '''simple docstring'''




      sys.stdout.write(str(SCREAMING_SNAKE_CASE     ) + end     )
      sys.stdout.flush()


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		Dict				,		SCREAMING_SNAKE_CASE	:		Union[str, Any]=""     ):







      '''simple docstring'''




      forceWrite(F'\u001b[{color}m{content}\u001b[0m'				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      forceWrite("""\r"""     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int				,		SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      forceWrite(F'\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'     )


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      forceWrite(""" """ * TERMINAL_WIDTH     )
      reset_cursor()






def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      reset_cursor()
      forceWrite("""-""" * TERMINAL_WIDTH     )













"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor

@require_torch
@require_vision
class 						lowercase   (   unittest.TestCase   ):





      @slow
      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            lowerCAmelCase    		=			AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            model.to(lowercase					)

            from datasets import load_dataset

            lowerCAmelCase    		=			load_dataset("""nielsr/rvlcdip-demo"""					)

            lowerCAmelCase    		=			dataset["""train"""][0]["""image"""].convert("""RGB"""					)

            lowerCAmelCase    		=			image_processor(lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)
                  lowerCAmelCase    		=			outputs.logits

            lowerCAmelCase    		=			torch.Size((1, 16)					)
            self.assertEqual(logits.shape ,		lowercase					)

            lowerCAmelCase    		=			torch.tensor(
                [-0.4_158, -0.4_092, -0.4_347] ,		device=lowercase ,		dtype=torch.float ,		)
            self.assertTrue(torch.allclose(logits[0, :3] ,		lowercase ,		atol=1e-4					)					)






